Bibliographic record
Abstract
Remote work or hybridized work has come to stay post COVID-19 pandemic, though not without both benefits and drawbacks. Work-life balance represents an essential need of remote workers, which could either be hindered or promoted during remote work. The continuous adoption of remote work warrants that the crucial challenge of work-life balance facing remote workers be addressed. Therefore, this study offers insights regarding the work-life balance processes of remote workers by following a research approach that involves synthesizing literature about remote work and work-life balance in an integrative way. This article upholds that the work-life balance processes of remote workers involve a work-life cognitive system that is responsible for maintaining equilibrium in the work-life system. The work-life system of remote workers includes elements, resources, forces, useful work, and output. Achieving balance between work and non-work domains is a function of the relative processes among the elements, resources, and forces within the work-life system. This article refers to the work-life balance cognitive system as ‘the work-life balance machine’. The work-life balance machine provides knowledge about how and what remote workers need to learn, adapt to, and change in the environment in order to develop an optimally balanced work and life. Thus, based on the blending of theories and concepts with the synthesized evidence from literature, this article offers a model; the work-life balance machine and a definition of work-life balance. In addition, this paper highlights actionable insights critical for human resource management in developing, supporting, and maintaining remote workers’ work-life balance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".